AAAI-96
Tutorial
on
Designing Computational Markets and Multiagent Organizations
Delivered in Portland, OR on 5 August 1996
Description
The recent explosion of internet activity and development of software agents
heralds a time when autonomous computational processes on wide-area networks
will be deployed on behalf of human users. Given their varying goals, capabilities,
and resources, computational agents will often find it
necessary to coordinate their activities to achieve desired results. The
problem facing designers of agents and interaction protocols is how to achieve
an allocation of activities and resources that best meets overall objectives,
without imposing centralized control. More generally, how can we relate
the global behavior of a collection of agents to the local behavior of individuals?
Given a large number of autonomous agents, each working with a limited view
of the overall situation and perhaps with conflicting goals, under what
conditions can we expect to produce good solutions to complex problems?
In this tutorial we address the fundamental problem of coordinating multiple
agents through the use of market mechanisms and organizational structures.
We present some relevant background in economics and organization theory
necessary to understand these systems, leading to some general design methodology
for constructing computational economies and organizations.
The methods are elaborated through case studies (e.g., networked information
services), computational experience, and discussion of key technical issues
(e.g., dynamic
behavior) underlying multiagent systems.
Presenters
Michael Wellman is an Assistant Professor in
the Department of Electrical Engineering and Computer Science at the
University
of Michigan. He received a PhD in Computer Science from the
Massachusetts Institute of Technology in 1988 for his work in qualitative
probabilistic reasoning and decision-theoretic planning. Current research
focuses on computational market mechanisms
for distributed decision making. In 1994, he received an NSF National Young
Investigator award.
Tad Hogg
is a member of the research staff at the Xerox
Palo Alto Research Center. His research interests include dynamics of
multiagent systems, the use of economic mechanisms for resource allocation,
cooperative problem solving and analogies with physical phase transitions
found in
combinatorial search problems. He holds a PhD in physics from Stanford University.